Guest Post by Willis Eschenbach
There’s a much-cited paper (129 citations) from 1994 called “On the Observed Near Cancellation between Longwave and Shortwave Cloud Forcing in Tropical Regions” by J. T. Kiehl (hereinafter Kiehl1994), available here. The paper makes the following claim (emphasis mine):
ABSTRACT
Observations based on Earth Radiation Budget Experiment (ERBE) satellite data indicate that there is a near cancellation between tropical longwave and shortwave cloud forcing in regions of deep convective activity. Cloud forcing depends on both cloud macrophysical properties (e.g., cloud amount, cloud height, etc.) and on microphysical properties (e.g., cloud particle size, particle shape, etc.). Hence, the near cancellation in the tropics could be due to either the macrophysical or the microphysical properties of these clouds, or a combination of these effects.
Now, to me that’s a pretty curious and surprising claim. The paper says that both in the Indonesian Region, as well as over the entire tropical Pacific, deep convective tropical clouds have no net effect on radiation, ostensibly because the longwave (positive) and reflected shortwave radiation (negative) cancel each other out. Let me call this the “cancellation hypothesis”. Kiehl supports the cancellation hypothesis inter alia with his Figure 1:
Figure 1. The first figure in Kiehl1994. The vertical axis shows shortwave cloud forcing, which is the amount of sunlight reflected by clouds in watts per square metre (W m-2). The horizontal axis shows the longwave cloud forcing, which is the change in top-of-atmosphere longwave forcing from the clouds. By convention, the reflected solar radiation is shown as negative, presumably because it is cooling the earth. Data is from April 1985.
And that looks pretty convincing … but, despite the 129 citations of the paper, I’m a suspicious fellow who believes firmly in the famous fallibility of experts. So I thought I’d use the CERES data and see if the cancellation hypothesis held up. Figure 2 shows that result, for the same Indonesian Region used by Kiehl.
Figure 2. Replication of the Kiehl study, using the ten-year April averages for the specified region to remove annual variations. Each square symbol represents a 1°x1° gridcell (N = 1500).
Instead of one single month’s data, I’ve used the averages of the ten Aprils in the CERES dataset.
Now, as you can see from Figure 2, the Kiehl hypothesis of cancellation has a big problem. The CERES results do not bear out Kiehl’s claims in the slightest. Instead, they support my hypothesis that increased tropical clouds cool the surface. As you can see, on average the loss from the reflected sunlight is about 20% or so greater than the gain from increased IR. This means that there is no cancellation. Instead, the clouds have a net cooling effect.
The paper goes on to say:
One feature of the cloud radiative forcing obtained from the Earth Radiation Budget Experiment ( ERBE) is the near cancellation between the longwave cloud forcing and the shortwave cloud forcing in tropical deep convective regions. This result was clearly shown by Kiehl and Ramanathan ( 1990) for the Indonesian convective region (here reproduced in Fig. 1 ). Further analysis of tropical deep convective regions of net cloud radiative forcing indicates that this is a ubiquitous feature that occurs over either ocean or land regions.
This is a more expansive claim than the one in the Abstract. Here the paper says that the cancellation happens over both land and ocean. So I thought I’d divide the Indonesian Region shown above into land and ocean regions. In addition, rather than average the months as in Figure 2, Figures 3 & 4 show all available April data for the entire time period. First, Figure 3 shows the land. I have colored the points by surface temperature of the gridcell.
Figure 3. As in Figure 2, but showing only the land. N=1,320.
Well, this shows that whatever might be happening in the Indonesian Region in the way of a linear relationship between reflected shortwave and longwave, it is definitely NOT happening over the land. The land shows little in the way of any relationship between shortwave and longwave.
Figure 4 shows the ocean data for the same Indonesian region.
Figure 4. As in Figure 2, but showing only the ocean. Note that the scale is slightly larger, to include all of the individual data. N=17,736
Well, this clarifies matters somewhat. First, about 40% of the land gridcells, but only about 10% of the ocean gridcells, have longwave cloud forcing greater than the shortwave forcing. Next, the land is pretty tightly clustered, with no apparent pattern. The ocean is different. Over the ocean the longwave is proportional to the shortwave … but it is a long ways from cancelling out. Instead, there’s a net cooling of -13.7 watts per square meter over the region. And the amount of cooling increases as the forcings increase. By the time the cloud reflections are up to 100 W m-2, the longwave is only up to 75 W m-2. That is a cooling from the clouds of about 25 W/m2, and not a cancellation under any meaning of the word.
In addition, there are a number of the warmest gridcells (red) which are on the left of the group (lots of reflection, little longwave).
Kiehl goes on to show his Figure 2, which compares the entire tropical Pacific region, from 10N 140E to 10S 90W. He shows a different kind of graph for this region, viz:
Figure 5. Kiehl Figure 2, showing the longwave and shortwave cloud forcing separately as functions of temperature.
Based on this graph, he makes the even more expansive claim that the cancellation of long-and shortwave radiation occurs across the Pacific, viz:
This figure illustrates that the cancellation between these two forcings occurs not only in the western tropical Pacific region of Fig. 1 but also across the entire tropical Pacific Ocean region. Even in regions of colder SSTs (298 K) the cancellation is apparent.
I found his style of graph in Figure 2 to be notably uninformative regarding the purported Pacific-wide cancellation. So I repeated his Figure 1 style of graph using the Pacific data, to see whether the forcings actually cancelled all across the Pacific. Figures 6 and 7 shows that result, for land and ocean, and reveals that there are large problems with this second claim as well.
Figure 6. As in Figure 2, but covering a larger area, the entire tropical Pacific Ocean as specified in the title. This figure shows land only. N=348.
Again, there is no clear pattern over the land, merely a cluster of data.
Figure 7. As in Figure 6, but covering ocean only. Note the slightly larger scale than in Figure 4. N=63,132.
This actually is pretty interesting. First off, again the ocean and land are different, with the land results clustered as in the Indonesian Region. Regarding the ocean, in only 5% of the gridcells does the longwave ever exceed the shortwave (area above the central diagonal line). In order for Kiehl’s cancellation hypothesis to be true, the average of a number of gridcells over an area would have to fall on the central diagonal line … which is clearly impossible with only 5% of them above the line.
Nor do things get better when we look at the entire dataset, and not just the April data. Figure 8 shows all of the data for the Pacific-wide area shown in Figure 7 (ocean only).
Figure 8. All months of data for the Pacific-wide area as in Figure 7
Next, rather than cancellation, there are a whole lot of gridcells where the longwave is smaller, and often much smaller, than the reflected shortwave. When there is solar reflection of a hundred watts per square metre, the longwave is only around sixty watts per square metre, for a full 40 W/m2 of cooling. And even on average the cooling is nearly 20W/m2 … not what we call cancellation on my planet.
Finally, his claim that “Even in regions of colder SSTs (298 K) the cancellation is apparent” is not upheld by the data. The temperature of 298 K [25°C] is shown in blue in Figure 7, and it is the farthest from cancellation of any of the data.
CONCLUSIONS
The main result is that the CERES data clearly and emphatically falsifies the cancellation hypothesis. In general, the longwave and shortwave are far from cancelling each other in the tropical deep convective areas.
A secondary result is that this clearly shows how the politicization of the field has affected the scientific process. Kiehl’s claims were very tempting to the theorists and modelers, because cancellation meant that they didn’t have to concern themselves with the deep convective processes, aka thunderstorms—the could simply repeat Kiehl’s claim that the shortwave and longwave cancelled each other out. And as a result, when more detailed data became available, the original claims of Kiehl1994 were never questioned.
Now, all we need is some automated method to notify the 129 people who cited the cancellation hypothesis in other scientific papers that the rumored cancellation has been cancelled for the duration …
All the best,
w.
Lovely work Willis.
The net radiative cancellation identified by Kiehl 1994 played a significant role in validating Hadley Centre’s climate model HadGEM1 – which was used in the IPCC Fourth Assessment Report.
In “The Physical Properties of the Atmosphere in the New Hadley Centre Global Environmental Model (HadGEM1). Part I: Model Description and Global Climatology
G. M. MARTIN, M. A. RINGER, V. D. POPE, A. JONES, C. DEARDEN, AND T. J. HINTON”, P1293 (Journal of Climate http://journals.ametsoc.org/doi/pdf/10.1175/JCLI3636.1 ) we have:
“The most marked improvement in the net CRF simulation in HadGAM1 is the representation of the near cancellation of shortwave and longwave CRF over the Tropics, particularly the tropical oceans (Kiehl 1994).”
Note: HadGAM1 is the atmosphere-only version of HadGEM1.
This means if Kiehl 1994 is wrong about CRF cancellation – then a significant error was baked into HadGEM1 and AR4.
I think the Fig. 2 of this paper confirms what Willis has done here.
https://www.eumetsat.int/website/wcm/idc/idcplg?IdcService=GET_FILE&dDocName=PDF_CONF_P41_S5_FUTYAN_V&RevisionSelectionMethod=LatestReleased&Rendition=Web
Willis,
Very interesting. Two comments:
1. Do papers which site the original 1994 paper also site more recent papers on the same subject? Andy Dessler’s papers on net cloud feed back and Troy Masters followup paper questioning the Dessler results seem related to your efforts here.
2. It might be informative to generate a 3-D graph with ocean temperature as the third axis and map a best-fit 2-D surface instead of the individual data points.
Forgot the one critical aspect which invalidates the analysis…
Coldish +1
Not be deterred by reality Grauiad gets 2014 off to a flying start with “at least 4C by 2100″.
http://www.theguardian.com/environment/2013/dec/31/planet-will-warm-4c-2100-climate
What is meant by “near cancellation”?
David A: “The LW energy mainly accelerates the evaporation of the surface, and had a much shorter residence time.”
That is a good point. So it may well get sent straight back up in the form of convection.
There is an unspoken assumption in all this cancellation/ not concellation stuff, that a watt is a watt, irrespective. Whereas a watt in the first 100um is not that same a watt penetrating 30m or more.
Very good point.
HenryP says: December 31, 2013 at 5:46 am “Your statement is true, but…
at night time clouds keeps earth warmer, specially in winter.
The GH effect, you know….”
I agree, which is why I specifically included the “daytime” in what I said, I remember a Summer in the UK in the 70s when we had literally weeks and weeks of cloud cover both day and night, but it didn’t rain.
That was the summer that never was, the temperatures hardly ever got above 70F and it was extremely depressing, it would actually have been better if it had rained occasionally.
George Steiner says: What is meant by “near cancellation”?
It means please ignore the biggest incertainty in climate modelling and go somewhere else so we can pretend it’s all based on fundamental physical laws and not back of envelop guesswork.
It means we have no idea how all this adds up so we’ll _assume_ it does not matter.
It means take one small area of ocean and look at one month of one year then draw conclusions for the whole of climate that we can extrapolate 100 years beyond the data.
Excellent question and analysis Willis!
It is a classic example of why independent replication of research is so critical as the replication confirms or falsifies research.
In the above case, that’s a solid thumping falsification of Kiehl1994.
The issue brought up is that you are almost required to publish.
a) To officially falsify Kiehl1994.
b) To cement your research as a fundamental cornerstone in climatology
c) To force climate research to recognize both their folly and their need to rethink research into forcings.
Publishing in Nature or whatever is not required. Only that you publish, perhaps even a paper here on WUWT, Climate Audit, wherever.
I’m sure that a number of worthwhile peers would sign on as peer reviewers and solidify the paper as ‘peer reviewed science’.
Willis,
Interesting stuff for sure! I think, to better ready this for any kind of peer review/critique, you need to address a few essential points. That being, “why is there such a difference between your results and those in 1985?” As well as introducing new data, as you have, you should also be able to say what may have been wrong with the old data/methodology. Leaving it open to the reader to jump to conclusions will result in a lot of conclusions and questions.
Discussions for example,
Data collection method between Ceres vs. ERBE…
Is there any upgrade to the sensor technology used to collect the data? What are the differences in collection methodology?
The Kiehl Paper looks at cloud top height/ temperature/ high ice albedo ..for example and you don’t discuss or address these implications.
Was the sample year 1985 in any way special?
Perhaps there was better correlation in 1985 – 25 years later, maybe it has changed significantly?
Here you would need to look at the yearly variation in the 10 year data set that you present.
Does any one of them look like 1985? Is there a gradual shift with time? Or was 1985 a high aerosol year? More or less cloud formation due to some other effect? Kiehl looked at variations in cloud height/formation as well as Enso and SST variations that you have not even addressed yet.
Anyway a good start I think, but you need to improve on the old work by saying what they missed , what you found instead and what is still missing in your work. I realise also WUWT is just a forum for shooting out (down?) new ideas and not a Science journal.
Good work, checking the uncheckable.
If one went through AR5, I wonder how many referenced works have been withdrawn or repudiated?
Have you considered colourcoding the temperature data not by temperature, but by date of capture? Where there is a background effect that has a time element, as in the PDO, you will see a shift in data within the dataset reflecting the other parameter.
Climate: Cloud Mixing Means Extra Global Warming
Doubling of atmospheric carbon dioxide points to the higher end of warming estimates.
A decline in ocean cloud cover projected in climate models points to more than 5.6°F (3°C) of global warming coming in this century, on the high end of past global warming estimates, warn climate scientists in a new study. (See also: “Global Warming Effects Map.”)
“This degree of warming would make large swaths of the tropics uninhabitable by humans and cause most forests at low and middle latitudes to change to something else,” says Steven Sherwood of Australia’s University of New South Wales, who led the study.
http://news.nationalgeographic.com/news/2013/12/131231-climate-sensitivity-doubling-carbon-warmer/
April only is enough. The slice of data is essentially the same period of the year for all points and for the time interval it produces over 60,000 points. That is enough for an accurate eyeball analysis of the claim that the shortwave and longwave cancel. Good work Willis.
My goodness, it’s like you are using some crazy method that puts the conclusions of one study to the test to see if the results are reproducible. You should call this the scientific method or something along those lines. Quick, everyone make the climate science community aware of this new technique so that they too can discard their method of ‘accept everything you read if it fits the agenda’. But seriously, good work Willis. CERES data is certainly turning out to be a bane to the CAGW church’s agenda.
Joe Born says:
December 31, 2013 at 4:45 am
Yes, the Kiehl paper identifies them as being the 2.5°x2.5° gridcell averages.
The way it’s done is slightly different than that, but close. They classify the instantaneous data as clear or not clear. The cloudy data is calculated as the difference between the “clear” and “all sky” conditions. And you are right that all such divisions have gray areas.
Joe, I strive to make my work accessible to everyone from the interested layman to the scientific specialist. How much and exactly what information to include are tricky questions. Too much, and the reader bails out. Not enough, and they want a better explanation of e.g. what the data are, or what the logic is, or …
In addition, what seems crystal clear from this side of my eyeballs is far too often somewhat vague or not specific enough when seen through the eyes of others.
In general, if I’m writing about a certain scientific paper, I don’t try to cover everything in the paper. I try to extract the most relevant quotes and data. Then I put in a link to the paper, and leave the interested reader to go as far as they wish.
All the best, and thanks as always for your comments.
w.
Paul Carter says:
December 31, 2013 at 6:33 am
That is an absolutely classic find, many thanks. I’m sure if we looked at the 129 citations of Kiehl’s work, we’d find some other interesting stuff …
w.
Kirk c says:
December 31, 2013 at 9:53 am
Say what? I have no idea what might be gained from that. A scientist is under no obligation to discover and identify the errors of other researchers. It is enough to falsify their results by presenting solid, accurate data and analysis.
In this case, being a suspicious fellow, I suspect that the issue was cherry-picked data. I mean, he used exactly one month’s worth of data to make his far-reaching claims, you do the math …
But how would I prove that, Kirk, and what difference would it possibly make? I neither know nor care what went wrong with their work. As long as nobody can falsify my work, the exact location where they went off of the rails is of little interest other than for the history of science.
w.
Willis,
Perhaps you can clear this up for me, not being an expert……How does your conclusion relate to (or go beyond and/or confirm) the conclusion published by Lindzen and Choi, 2009, “On the Determination of Climate Feedbacks from ERBE data [and CERES data]” (Geophy. Res. Lett. v.36, p. L16705-..)? From Lindzen-Choi abstract: “It appears, for the entire tropics, the observed outgoing radiation fluxes increase with the increase in sea surface tempertures (SSTs). The observed behavior of radiation fluxes implies negative feedback processes associated with the relatively low climate sensitivity…Results also show, the feedback in ERBE is mostly from shortwave radiation while the feedback in the models (GCMs) is mostly from longwave radiation….constituting a very fundamental problem in climate prediction.” They illustrate that the total feedback, LW+SW was less than minus one, which I interpret as more heat is lost than gained, or opposite of climate models.
Did you correct for the known bias in the CERES data? Also, did you filter the data to exclude grid cells dominated by low, non-convective clouds? (Both issues are discussed in the paper below.)
http://journals.ametsoc.org/doi/abs/10.1175/1520-0442%282004%29017%3C3192%3ACRFIPA%3E2.0.CO%3B2
The bias adds about 10 W/m^2 to the cloud forcing, making it appear as though clouds cool more than they do (which would change your figure in yellow above). The failure to filter out low, non-convective clouds would obviously bias your data (because they cool more than they provide LW forcing), and is a first-order QA thing you should do if you are seriously considering submitting this for publication.
Anyway, the Iris Hypothesis is a couple of decades old so this is not really new stuff. Lindzen fought, and lost mostly, this battle a decade ago.
A C Osborn says
http://wattsupwiththat.com/2013/12/30/cancelling-the-tropical-cancellation/#comment-1519009
Henry says
True enough, I found the wave in New & Old England runs opposite of the global sine wave
meaning they get the clouds and the “WARMER” weather during a cooling period and “COOLER” weather during a warming period.
We are currently cooling, so the weather would be depressing.
Just take a holiday to South Africa, where the sun always shines (most of the time)
Wishing you all at WUWT a blessed 2014
“I neither know nor care what went wrong with their work”
Maybe just the 85 La-Nina?
Willis:
Fascinating.
So looking at the data honestly reveals:
1) cooler SST, not much heat goes to space. Fewer clouds more solar heating.
2) warmer SST, a lot more heat goes to space. More clouds, but less solar heating.
Hypothesis
3)Very Hot SST (hypothetically 35C), much more heat goes to space. Much more clouds, difficult for solar heating. Perfect cancellation or better with much warmer SST?
Willis well done a nice bit of work. Your response to Kirk is also spot on but unless you publish in peer-reviewed journals it’s – unfortunately – not going to be taken seriously.
You say peer-review isn’t worth the effort but a letter to the editor (although sometimes there is a cutoff for response) might be. Good stuff all the same.